ESTIMULANTES EN LA GERMINACIÓN Y BIOMETRÍA INICIAL DE DOS VARIEDADES DE MAÍZ MORADO (Zea mays L.)

1 Setup

Instalar version en desarrollo.

if (!require("remotes"))
  install.packages("remotes")
remotes::install_github("flavjack/inti")

library(emmeans)
library(corrplot)
library(multcomp)
library(factoextra)
library(corrplot)
library(png)
source('https://inkaverse.com/setup.r')

cat("Project: ", getwd())

Project:  C:/INIA/GIT/prochira_maiz_morado

session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14 ucrt)
 os       Windows 11 x64 (build 22631)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  Spanish_Peru.utf8
 ctype    Spanish_Peru.utf8
 tz       America/Lima
 date     2024-08-19
 pandoc   3.1.11 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version  date (UTC) lib source
 agricolae       1.3-7    2023-10-22 [1] CRAN (R 4.4.0)
 AlgDesign       1.2.1    2022-05-25 [1] CRAN (R 4.4.0)
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 glue            1.7.0    2024-01-09 [1] CRAN (R 4.4.0)
 googledrive   * 2.1.1    2023-06-11 [1] CRAN (R 4.4.0)
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 gsheet        * 0.4.5    2020-04-07 [1] CRAN (R 4.4.0)
 gtable          0.3.5    2024-04-22 [1] CRAN (R 4.4.0)
 hms             1.1.3    2023-03-21 [1] CRAN (R 4.4.0)
 htmltools       0.5.8.1  2024-04-04 [1] CRAN (R 4.4.0)
 htmlwidgets     1.6.4    2023-12-06 [1] CRAN (R 4.4.0)
 httpuv          1.6.15   2024-03-26 [1] CRAN (R 4.4.0)
 httr            1.4.7    2023-08-15 [1] CRAN (R 4.4.0)
 huito         * 0.2.4    2023-10-25 [1] CRAN (R 4.4.0)
 inti          * 0.6.5    2024-08-02 [1] Github (flavjack/inti@38be898)
 jsonlite        1.8.8    2023-12-04 [1] CRAN (R 4.4.0)
 knitr         * 1.46     2024-04-06 [1] CRAN (R 4.4.0)
 later           1.3.2    2023-12-06 [1] CRAN (R 4.4.0)
 lattice         0.22-6   2024-03-20 [2] CRAN (R 4.4.1)
 leaps           3.1      2020-01-16 [1] CRAN (R 4.4.0)
 lifecycle       1.0.4    2023-11-07 [1] CRAN (R 4.4.0)
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 lubridate     * 1.9.3    2023-09-27 [1] CRAN (R 4.4.0)
 magick        * 2.8.3    2024-02-18 [1] CRAN (R 4.4.0)
 magrittr        2.0.3    2022-03-30 [1] CRAN (R 4.4.0)
 MASS          * 7.3-60.2 2024-04-26 [2] CRAN (R 4.4.1)
 Matrix          1.7-0    2024-04-26 [2] CRAN (R 4.4.1)
 memoise         2.0.1    2021-11-26 [1] CRAN (R 4.4.0)
 mime            0.12     2021-09-28 [1] CRAN (R 4.4.0)
 miniUI          0.1.1.1  2018-05-18 [1] CRAN (R 4.4.0)
 minqa           1.2.7    2024-05-20 [1] CRAN (R 4.4.0)
 mnormt          2.1.1    2022-09-26 [1] CRAN (R 4.4.0)
 multcomp      * 1.4-25   2023-06-20 [1] CRAN (R 4.4.0)
 multcompView    0.1-10   2024-03-08 [1] CRAN (R 4.4.0)
 munsell         0.5.1    2024-04-01 [1] CRAN (R 4.4.0)
 mvtnorm       * 1.2-5    2024-05-21 [1] CRAN (R 4.4.0)
 nlme            3.1-164  2023-11-27 [2] CRAN (R 4.4.1)
 nloptr          2.0.3    2022-05-26 [1] CRAN (R 4.4.0)
 openssl         2.2.0    2024-05-16 [1] CRAN (R 4.4.0)
 pillar          1.9.0    2023-03-22 [1] CRAN (R 4.4.0)
 pkgbuild        1.4.4    2024-03-17 [1] CRAN (R 4.4.0)
 pkgconfig       2.0.3    2019-09-22 [1] CRAN (R 4.4.0)
 pkgload         1.3.4    2024-01-16 [1] CRAN (R 4.4.0)
 png           * 0.1-8    2022-11-29 [1] CRAN (R 4.4.0)
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 promises        1.3.0    2024-04-05 [1] CRAN (R 4.4.0)
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 purrr         * 1.0.2    2023-08-10 [1] CRAN (R 4.4.0)
 R6              2.5.1    2021-08-19 [1] CRAN (R 4.4.0)
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 Rcpp            1.0.12   2024-01-09 [1] CRAN (R 4.4.0)
 readr         * 2.1.5    2024-01-10 [1] CRAN (R 4.4.0)
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 sessioninfo     1.2.2    2021-12-06 [1] CRAN (R 4.4.0)
 shiny         * 1.8.1.1  2024-04-02 [1] CRAN (R 4.4.0)
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 showtextdb      3.0      2020-06-04 [1] CRAN (R 4.4.0)
 stringi         1.8.4    2024-05-06 [1] CRAN (R 4.4.0)
 stringr       * 1.5.1    2023-11-14 [1] CRAN (R 4.4.0)
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 TH.data       * 1.1-2    2023-04-17 [1] CRAN (R 4.4.0)
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 usethis       * 2.2.3    2024-02-19 [1] CRAN (R 4.4.0)
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 xtable          1.8-4    2019-04-21 [1] CRAN (R 4.4.0)
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 zoo             1.8-12   2023-04-13 [1] CRAN (R 4.4.0)

 [1] C:/Users/INIA/AppData/Local/R/win-library/4.4
 [2] C:/Program Files/R/R-4.4.1/library

──────────────────────────────────────────────────────────────────────────────

2 Refrencias

• (PCA) https://www.r-bloggers.com/2017/07/pca-course-using-factominer/
• (PCA) https://www.youtube.com/watch?v=Uhw-1NilmAk&ab_channel=Fran%C3%A7oisHusson
• (HCPC) https://youtu.be/EJqYTDTJJug

3 Import data

https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741

url <- "https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741"

gs <- url %>% 
  as_sheets_id()

imbibition <- gs %>% 
  range_read("imbibition") %>% 
  rename_with(~ tolower(.)) %>% 
  mutate(time = tiempo, .after = tiempo) %>% 
  mutate(variedad = case_when(
    variedad %in% c("criollo") ~ "Creole"
    , variedad %in% c("Hibrido") ~ "Hybrid"
  )) %>% 
  mutate(across(1:tiempo, ~ as.factor(.))) 

str(imbibition)
## tibble [2,100 × 7] (S3: tbl_df/tbl/data.frame)
##  $ bloque     : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
##  $ trat       : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ variedad   : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
##  $ tiempo     : Factor w/ 5 levels "0","3","6","9",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ time       : num [1:2100] 0 0 0 0 0 0 0 0 0 0 ...
##  $ peso       : num [1:2100] 0.58 0.62 0.73 0.72 0.72 0.68 0.71 0.61 0.69 0.64 ...

germination <- gs %>% 
  range_read("germination") %>% 
  rename_with(~ tolower(.)) %>% 
  mutate(variedad = case_when(
    variedad %in% c("criollo") ~ "Creole"
    , variedad %in% c("Hibrido") ~ "Hybrid"
  )) %>% 
  mutate(trat = case_when(
    tratamiento %in% "Agua Destilada" ~ "T0"
    , tratamiento %in% "Algas Marinas 1 L/cil" ~ "T1"
    , tratamiento %in% "Algas Marinas 1,5 L/cil" ~ "T2"
    , tratamiento %in% "Azufre 100 gr.200 L-1" ~ "T3"
    , tratamiento %in% "Azufre 150 gr.200 L-1" ~ "T4"
    , tratamiento %in% "Suero de leche 10%" ~ "T5"
    , tratamiento %in% "Suero de leche 30%" ~ "T6"
  ), .before = tratamiento) %>% 
  mutate(across(1:variedad, ~ as.factor(.))) 

str(germination)
## tibble [42 × 11] (S3: tbl_df/tbl/data.frame)
##  $ bloque     : Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 1 2 3 1 ...
##  $ trat       : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 2 2 2 3 3 3 4 ...
##  $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 2 2 2 3 3 3 4 ...
##  $ variedad   : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dia 1      : num [1:42] 2 4 3 0 1 1 0 0 1 0 ...
##  $ dia 2      : num [1:42] 5 4 5 3 2 1 1 4 5 1 ...
##  $ dia 3      : num [1:42] 1 1 1 1 0 0 0 0 0 0 ...
##  $ total      : num [1:42] 8 9 9 4 3 2 1 4 6 1 ...
##  $ pg         : num [1:42] 80 90 90 40 30 20 10 40 60 10 ...
##  $ vg         : num [1:42] 2.67 3 3 2 1.5 ...
##  $ ig         : num [1:42] 2.4 2.7 2.7 0.8 0.6 0.4 0.1 0.4 1.2 0.1 ...

plantula <- gs %>% 
  range_read("plantula") %>% 
  rename_with(~ tolower(.)) %>% 
  mutate(variedad = case_when(
    variedad %in% c("criollo") ~ "Creole"
    , variedad %in% c("hibrido") ~ "Hybrid"
  )) %>% 
  mutate(across(1:variedad, ~ as.factor(.)))

str(plantula)
## tibble [210 × 17] (S3: tbl_df/tbl/data.frame)
##  $ bloque         : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 2 2 2 2 2 ...
##  $ trat           : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ tratamiento    : Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ variedad       : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
##  $ raiz_lgtd      : num [1:210] 11 8 12 11 8 13 10 12 9 13 ...
##  $ gsr_raiz       : num [1:210] 1.3 1.19 1.51 1.21 1.17 1.13 1.68 1.27 1.03 1.16 ...
##  $ num_raiz       : num [1:210] 8 11 11 9 12 16 10 9 16 11 ...
##  $ peso_fres_raiz : num [1:210] 4.82 3.21 4.91 4.42 4.62 6.07 4.97 6.13 3.05 4 ...
##  $ peso_seco_raiz : num [1:210] 0.73 0.41 0.62 0.66 0.72 0.54 0.75 0.56 0.57 0.74 ...
##  $ alt_planta     : num [1:210] 30 26 28 32 25 27 28 35 29 29 ...
##  $ gsr_tallo      : num [1:210] 5.86 4.56 6.59 4.63 4.55 4.14 4.02 4.32 3.45 3.61 ...
##  $ nhp_hoja       : num [1:210] 5 5 5 6 4 5 5 5 5 5 ...
##  $ larg_hoja      : num [1:210] 26 23 21 27 29 22 24 30 25 23 ...
##  $ grs_hoja       : num [1:210] 0.94 1.15 0.89 0.98 1.01 0.72 0.62 1.03 0.71 1.34 ...
##  $ anch_hoja      : num [1:210] 19.3 19.9 21.5 17.3 18.9 ...
##  $ peso_fres_brote: num [1:210] 5.34 5.99 5.45 4.81 7.03 6.79 4.99 4.53 3.56 4 ...
##  $ peso_seco_brote: num [1:210] 0.5 0.49 1.04 0.78 0.68 0.67 0.69 0.78 0.73 0.75 ...

4 Tratamientos

imbibition %>% 
  group_by(trat, tratamiento) %>% 
  summarise(n = n()) %>% 
  select(!n)
## # A tibble: 7 × 2
## # Groups:   trat [7]
##   trat  tratamiento            
##   <fct> <fct>                  
## 1 T0    Agua Destilada         
## 2 T1    Algas Marinas 1 L/cil  
## 3 T2    Algas Marinas 1,5 L/cil
## 4 T3    Azufre 100 gr.200 L-1  
## 5 T4    Azufre 150 gr.200 L-1  
## 6 T5    Suero de leche 10%     
## 7 T6    Suero de leche 30%

5 Data summary

sm <- imbibition %>% 
  group_by(tratamiento, variedad, tiempo) %>% 
  summarise(across(peso, ~ sum(!is.na(.))))

sm
## # A tibble: 70 × 4
## # Groups:   tratamiento, variedad [14]
##    tratamiento    variedad tiempo  peso
##    <fct>          <fct>    <fct>  <int>
##  1 Agua Destilada Creole   0         30
##  2 Agua Destilada Creole   3         30
##  3 Agua Destilada Creole   6         30
##  4 Agua Destilada Creole   9         30
##  5 Agua Destilada Creole   12        30
##  6 Agua Destilada Hybrid   0         30
##  7 Agua Destilada Hybrid   3         30
##  8 Agua Destilada Hybrid   6         30
##  9 Agua Destilada Hybrid   9         30
## 10 Agua Destilada Hybrid   12        30
## # ℹ 60 more rows

sm <- germination %>% 
  group_by(tratamiento, variedad) %>% 
  summarise(across(pg:ig, ~ sum(!is.na(.))))

sm
## # A tibble: 14 × 5
## # Groups:   tratamiento [7]
##    tratamiento             variedad    pg    vg    ig
##    <fct>                   <fct>    <int> <int> <int>
##  1 Agua Destilada          Creole       3     3     3
##  2 Agua Destilada          Hybrid       3     3     3
##  3 Algas Marinas 1 L/cil   Creole       3     3     3
##  4 Algas Marinas 1 L/cil   Hybrid       3     3     3
##  5 Algas Marinas 1,5 L/cil Creole       3     3     3
##  6 Algas Marinas 1,5 L/cil Hybrid       3     3     3
##  7 Azufre 100 gr.200 L-1   Creole       3     3     3
##  8 Azufre 100 gr.200 L-1   Hybrid       3     3     3
##  9 Azufre 150 gr.200 L-1   Creole       3     3     3
## 10 Azufre 150 gr.200 L-1   Hybrid       3     3     3
## 11 Suero de leche 10%      Creole       3     3     3
## 12 Suero de leche 10%      Hybrid       3     3     3
## 13 Suero de leche 30%      Creole       3     3     3
## 14 Suero de leche 30%      Hybrid       3     3     3

sm <- plantula %>% 
  group_by(tratamiento, variedad) %>% 
  summarise(across(where(is.numeric), ~ sum(!is.na(.))))

sm
## # A tibble: 14 × 15
## # Groups:   tratamiento [7]
##    tratamiento             variedad raiz_lgtd gsr_raiz num_raiz peso_fres_raiz
##    <fct>                   <fct>        <int>    <int>    <int>          <int>
##  1 Agua Destilada          Creole          15       15       15             15
##  2 Agua Destilada          Hybrid          15       15       15             15
##  3 Algas Marinas 1 L/cil   Creole          15       15       15             15
##  4 Algas Marinas 1 L/cil   Hybrid          15       15       15             15
##  5 Algas Marinas 1,5 L/cil Creole          15       15       15             15
##  6 Algas Marinas 1,5 L/cil Hybrid          15       15       15             15
##  7 Azufre 100 gr.200 L-1   Creole          15       15       15             15
##  8 Azufre 100 gr.200 L-1   Hybrid          15       15       15             15
##  9 Azufre 150 gr.200 L-1   Creole          15       15       15             15
## 10 Azufre 150 gr.200 L-1   Hybrid          15       15       15             15
## 11 Suero de leche 10%      Creole          15       15       15             15
## 12 Suero de leche 10%      Hybrid          15       15       15             15
## 13 Suero de leche 30%      Creole          15       15       15             15
## 14 Suero de leche 30%      Hybrid          15       15       15             15
## # ℹ 9 more variables: peso_seco_raiz <int>, alt_planta <int>, gsr_tallo <int>,
## #   nhp_hoja <int>, larg_hoja <int>, grs_hoja <int>, anch_hoja <int>,
## #   peso_fres_brote <int>, peso_seco_brote <int>

6 Objetivos

1. Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.

2. Identificar el mejor tratamiento que influye positivamente en el crecimiento y desarrollo de plantulas en el cultivo de Maíz morado.

6.1 Objetivo Específico 1

Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.

• Imbibiciación, % germinación, velocidad e IG

6.1.1 Imbibición

trait <- "peso"
fb <- imbibition

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad +  (1 + tiempo|tratamiento)") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + tiempo +  trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers %>% kable()

index	bloque	trat	variedad	tiempo	tratamiento	peso	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: peso
##                 Df  Sum Sq Mean Sq  F value              Pr(>F)    
## bloque           2  0.0021 0.00105   0.1222               0.885    
## tiempo           4 10.0058 2.50146 289.7715 <0.0000000000000002 ***
## trat             6  3.2174 0.53624  62.1186 <0.0000000000000002 ***
## variedad         1  0.6165 0.61649  71.4150 <0.0000000000000002 ***
## trat:variedad    6  2.6467 0.44111  51.0987 <0.0000000000000002 ***
## Residuals     2080 17.9556 0.00863                                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ tiempo|variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	tiempo	variedad	trat	emmean	SE	df	lower.CL	upper.CL	group
1	12	Creole	T0	0.8296986	0.0086019	2080	0.8128293	0.8465678	a
3	9	Creole	T0	0.8279129	0.0086019	2080	0.8110436	0.8447821	a
2	3	Creole	T0	0.7645081	0.0086019	2080	0.7476388	0.7813774	b
4	6	Creole	T0	0.7620295	0.0086019	2080	0.7451603	0.7788988	b
5	0	Creole	T0	0.6398176	0.0086019	2080	0.6229483	0.6566869	c
11	12	Creole	T1	0.7157719	0.0086019	2080	0.6989026	0.7326412	a
13	9	Creole	T1	0.7139862	0.0086019	2080	0.6971169	0.7308555	a
12	3	Creole	T1	0.6505814	0.0086019	2080	0.6337122	0.6674507	b
14	6	Creole	T1	0.6481029	0.0086019	2080	0.6312336	0.6649721	b
15	0	Creole	T1	0.5258910	0.0086019	2080	0.5090217	0.5427602	c
21	12	Creole	T2	0.6749719	0.0086019	2080	0.6581026	0.6918412	a
23	9	Creole	T2	0.6731862	0.0086019	2080	0.6563169	0.6900555	a
22	3	Creole	T2	0.6097814	0.0086019	2080	0.5929122	0.6266507	b
24	6	Creole	T2	0.6073029	0.0086019	2080	0.5904336	0.6241721	b
25	0	Creole	T2	0.4850910	0.0086019	2080	0.4682217	0.5019602	c
31	12	Creole	T3	0.6591052	0.0086019	2080	0.6422360	0.6759745	a
33	9	Creole	T3	0.6573195	0.0086019	2080	0.6404503	0.6741888	a
32	3	Creole	T3	0.5939148	0.0086019	2080	0.5770455	0.6107840	b
34	6	Creole	T3	0.5914362	0.0086019	2080	0.5745669	0.6083055	b
35	0	Creole	T3	0.4692243	0.0086019	2080	0.4523550	0.4860936	c
41	12	Creole	T4	0.6322386	0.0086019	2080	0.6153693	0.6491078	a
43	9	Creole	T4	0.6304529	0.0086019	2080	0.6135836	0.6473221	a
42	3	Creole	T4	0.5670481	0.0086019	2080	0.5501788	0.5839174	b
44	6	Creole	T4	0.5645695	0.0086019	2080	0.5477003	0.5814388	b
45	0	Creole	T4	0.4423576	0.0086019	2080	0.4254883	0.4592269	c
51	12	Creole	T5	0.8092386	0.0086019	2080	0.7923693	0.8261078	a
53	9	Creole	T5	0.8074529	0.0086019	2080	0.7905836	0.8243221	a
52	3	Creole	T5	0.7440481	0.0086019	2080	0.7271788	0.7609174	b
54	6	Creole	T5	0.7415695	0.0086019	2080	0.7247003	0.7584388	b
55	0	Creole	T5	0.6193576	0.0086019	2080	0.6024883	0.6362269	c
61	12	Creole	T6	0.7740386	0.0086019	2080	0.7571693	0.7909078	a
63	9	Creole	T6	0.7722529	0.0086019	2080	0.7553836	0.7891221	a
62	3	Creole	T6	0.7088481	0.0086019	2080	0.6919788	0.7257174	b
64	6	Creole	T6	0.7063695	0.0086019	2080	0.6895003	0.7232388	b
65	0	Creole	T6	0.5841576	0.0086019	2080	0.5672883	0.6010269	c
6	12	Hybrid	T0	0.7764386	0.0086019	2080	0.7595693	0.7933078	a
8	9	Hybrid	T0	0.7746529	0.0086019	2080	0.7577836	0.7915221	a
7	3	Hybrid	T0	0.7112481	0.0086019	2080	0.6943788	0.7281174	b
9	6	Hybrid	T0	0.7087695	0.0086019	2080	0.6919003	0.7256388	b
10	0	Hybrid	T0	0.5865576	0.0086019	2080	0.5696883	0.6034269	c
16	12	Hybrid	T1	0.7279719	0.0086019	2080	0.7111026	0.7448412	a
18	9	Hybrid	T1	0.7261862	0.0086019	2080	0.7093169	0.7430555	a
17	3	Hybrid	T1	0.6627814	0.0086019	2080	0.6459122	0.6796507	b
19	6	Hybrid	T1	0.6603029	0.0086019	2080	0.6434336	0.6771721	b
20	0	Hybrid	T1	0.5380910	0.0086019	2080	0.5212217	0.5549602	c
26	12	Hybrid	T2	0.7881052	0.0086019	2080	0.7712360	0.8049745	a
28	9	Hybrid	T2	0.7863195	0.0086019	2080	0.7694503	0.8031888	a
27	3	Hybrid	T2	0.7229148	0.0086019	2080	0.7060455	0.7397840	b
29	6	Hybrid	T2	0.7204362	0.0086019	2080	0.7035669	0.7373055	b
30	0	Hybrid	T2	0.5982243	0.0086019	2080	0.5813550	0.6150936	c
36	12	Hybrid	T3	0.7332386	0.0086019	2080	0.7163693	0.7501078	a
38	9	Hybrid	T3	0.7314529	0.0086019	2080	0.7145836	0.7483221	a
37	3	Hybrid	T3	0.6680481	0.0086019	2080	0.6511788	0.6849174	b
39	6	Hybrid	T3	0.6655695	0.0086019	2080	0.6487003	0.6824388	b
40	0	Hybrid	T3	0.5433576	0.0086019	2080	0.5264883	0.5602269	c
46	12	Hybrid	T4	0.7735052	0.0086019	2080	0.7566360	0.7903745	a
48	9	Hybrid	T4	0.7717195	0.0086019	2080	0.7548503	0.7885888	a
47	3	Hybrid	T4	0.7083148	0.0086019	2080	0.6914455	0.7251840	b
49	6	Hybrid	T4	0.7058362	0.0086019	2080	0.6889669	0.7227055	b
50	0	Hybrid	T4	0.5836243	0.0086019	2080	0.5667550	0.6004936	c
56	12	Hybrid	T5	0.7615719	0.0086019	2080	0.7447026	0.7784412	a
58	9	Hybrid	T5	0.7597862	0.0086019	2080	0.7429169	0.7766555	a
57	3	Hybrid	T5	0.6963814	0.0086019	2080	0.6795122	0.7132507	b
59	6	Hybrid	T5	0.6939029	0.0086019	2080	0.6770336	0.7107721	b
60	0	Hybrid	T5	0.5716910	0.0086019	2080	0.5548217	0.5885602	c
66	12	Hybrid	T6	0.7741052	0.0086019	2080	0.7572360	0.7909745	a
68	9	Hybrid	T6	0.7723195	0.0086019	2080	0.7554503	0.7891888	a
67	3	Hybrid	T6	0.7089148	0.0086019	2080	0.6920455	0.7257840	b
69	6	Hybrid	T6	0.7064362	0.0086019	2080	0.6895669	0.7233055	b
70	0	Hybrid	T6	0.5842243	0.0086019	2080	0.5673550	0.6010936	c

p1a <- mc %>% 
  plot_smr(type = "line"
           , x = "tiempo"
           , y = "emmean"
           , group = "trat"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Seed weight (g)"
           , xlab = "Time (h)"
           , glab = "Treatment"
           , ylimits = c(0.4, 1, 0.2)
           ) + 
  facet_wrap(. ~ variedad, ncol = 2) +
  theme(legend.position = "top") +
  guides(colour = guide_legend(nrow = 1))

p1a

6.1.2 Porcentaje de Germination

trait <- "pg"
fb <- germination

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers %>% kable()

index	bloque	trat	variedad	pg	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: pg
##               Df  Sum Sq Mean Sq F value   Pr(>F)   
## bloque         2   633.3   316.7  0.6222 0.544582   
## trat           6  7000.0  1166.7  2.2922 0.065673 . 
## variedad       1  4609.5  4609.5  9.0565 0.005753 **
## trat:variedad  6  6857.1  1142.9  2.2454 0.070466 . 
## Residuals     26 13233.3   509.0                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	variedad	trat	emmean	SE	df	lower.CL	upper.CL	group
2	Creole	T0	86.66667	13.02529	26	59.8928044	113.44053	a
1	Hybrid	T0	63.33333	13.02529	26	36.5594710	90.10720	a
3	Hybrid	T1	70.00000	13.02529	26	43.2261377	96.77386	a
4	Creole	T1	30.00000	13.02529	26	3.2261377	56.77386	b
5	Hybrid	T2	56.66667	13.02529	26	29.8928044	83.44053	a
6	Creole	T2	36.66667	13.02529	26	9.8928044	63.44053	a
7	Hybrid	T3	66.66667	13.02529	26	39.8928044	93.44053	a
8	Creole	T3	16.66667	13.02529	26	-10.1071956	43.44053	b
9	Hybrid	T4	76.66667	13.02529	26	49.8928044	103.44053	a
10	Creole	T4	26.66667	13.02529	26	-0.1071956	53.44053	b
11	Hybrid	T5	70.00000	13.02529	26	43.2261377	96.77386	a
12	Creole	T5	70.00000	13.02529	26	43.2261377	96.77386	a
13	Hybrid	T6	43.33333	13.02529	26	16.5594710	70.10720	a
14	Creole	T6	33.33333	13.02529	26	6.5594710	60.10720	a

p1b <- mc %>% 
  plot_smr(type = "bar"
           , x = "trat"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Germination ('%')"
           , xlab = "Treatments"
           , glab = "Variety"
           , ylimits = c(0, 120, 20)
           ) 

p1b

6.1.3 Velocidad de germinación

trait <- "vg"
fb <- germination

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers %>% kable()

	index	bloque	trat	variedad	vg	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag
7	7	1	T2	Creole	1	-1.666667	-3.372454	0.0007450	0.0007450159	0.0305456	OUTLIER
34	34	1	T4	Hybrid	5	1.888889	3.822114	0.0001323	0.0001323123	0.0055571	OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: vg
##               Df  Sum Sq Mean Sq F value   Pr(>F)   
## bloque         2  1.3321 0.66607  1.3664 0.274150   
## trat           6  8.6594 1.44323  2.9608 0.026214 * 
## variedad       1  2.0003 2.00025  4.1035 0.054051 . 
## trat:variedad  6 11.5622 1.92703  3.9533 0.006872 **
## Residuals     24 11.6989 0.48745                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	variedad	trat	emmean	SE	df	lower.CL	upper.CL	group
2	Creole	T0	2.888889	0.4030931	24	2.0569455	3.720832	a
1	Hybrid	T0	2.333333	0.4030931	24	1.5013900	3.165277	a
3	Hybrid	T1	3.277778	0.4030931	24	2.4458344	4.109721	a
4	Creole	T1	1.500000	0.4030931	24	0.6680567	2.331943	b
6	Creole	T2	3.379630	0.5004960	24	2.3466566	4.412603	a
5	Hybrid	T2	2.833333	0.4030931	24	2.0013900	3.665277	a
7	Hybrid	T3	3.833333	0.4030931	24	3.0013900	4.665277	a
8	Creole	T3	1.666667	0.4030931	24	0.8347233	2.498610	b
9	Hybrid	T4	2.046296	0.5004960	24	1.0133232	3.079269	a
10	Creole	T4	1.333333	0.4030931	24	0.5013900	2.165277	a
12	Creole	T5	3.055556	0.4030931	24	2.2236122	3.887499	a
11	Hybrid	T5	3.000000	0.4030931	24	2.1680567	3.831943	a
14	Creole	T6	2.333333	0.4030931	24	1.5013900	3.165277	a
13	Hybrid	T6	1.833333	0.4030931	24	1.0013900	2.665277	a

p1c <- mc %>% 
  plot_smr(type = "bar"
           , x = "trat"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Germination speed (days)"
           , xlab = "Treatments"
           , glab = "Variety"
           , ylimits = c(0, 6, 1)
           ) 

p1c

6.1.4 Indice de germinación

trait <- "ig"
fb <- germination

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers %>% kable()

	index	bloque	trat	variedad	ig	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag
25	25	1	T1	Hybrid	0.2	-1.466667	-3.29751	0.0009755	0.0009754607	0.0409693	OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: ig
##               Df  Sum Sq Mean Sq F value   Pr(>F)   
## bloque         2  0.3050  0.1525  0.4149 0.664896   
## trat           6 10.3540  1.7257  4.6949 0.002507 **
## variedad       1  3.8850  3.8850 10.5697 0.003278 **
## trat:variedad  6  6.5489  1.0915  2.9695 0.024965 * 
## Residuals     25  9.1890  0.3676                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	variedad	trat	emmean	SE	df	lower.CL	upper.CL	group
2	Creole	T0	2.6000000	0.3500293	25	1.8791012	3.3208988	a
1	Hybrid	T0	1.7666667	0.3500293	25	1.0457678	2.4875655	a
3	Hybrid	T1	2.3679487	0.4341579	25	1.4737838	3.2621137	a
4	Creole	T1	0.6000000	0.3500293	25	-0.1208988	1.3208988	b
5	Hybrid	T2	1.1333333	0.3500293	25	0.4124345	1.8542322	a
6	Creole	T2	0.5666667	0.3500293	25	-0.1542322	1.2875655	a
7	Hybrid	T3	1.2333333	0.3500293	25	0.5124345	1.9542322	a
8	Creole	T3	0.1666667	0.3500293	25	-0.5542322	0.8875655	b
9	Hybrid	T4	1.9666667	0.3500293	25	1.2457678	2.6875655	a
10	Creole	T4	0.5333333	0.3500293	25	-0.1875655	1.2542322	b
11	Hybrid	T5	1.7000000	0.3500293	25	0.9791012	2.4208988	a
12	Creole	T5	1.6666667	0.3500293	25	0.9457678	2.3875655	a
13	Hybrid	T6	1.0666667	0.3500293	25	0.3457678	1.7875655	a
14	Creole	T6	0.5333333	0.3500293	25	-0.1875655	1.2542322	a

p1d <- mc %>% 
  plot_smr(type = "bar"
           , x = "trat"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Germination Index"
           , xlab = "Treatments"
           , glab = "Variety"
           , ylimits = c(0, 5, 1)
           ) 

p1d

6.2 Figura 1

legend <- cowplot::get_plot_component(p1b, 'guide-box-top', return_all = TRUE)

p1i <- list(p1b + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1c + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1d + labs(x = NULL) + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 1
            , labels = c("b", "c", "d")
            ) 

p1il <- list(legend, p1i) %>% 
  plot_grid(plotlist = ., ncol = 1, align = 'v', rel_heights = c(0.05, 1))


plot <- list(p1a, p1il) %>% 
  plot_grid(plotlist = .
            , ncol = 1
            , labels = c("a")
            , rel_heights = c(0.6, 1)
            )  
  
plot %>% 
  ggsave2(plot = ., "files/Fig-1.jpg"
         , units = "cm"
         , width = 24
         , height = 29
         )

plot %>% 
  ggsave2(plot = ., "files/Fig-1.eps"
         , units = "cm"
         , width = 24
         , height = 29
         )

knitr::include_graphics("files/Fig-1.jpg")

6.3 Objetivo Específico 2

Identificar el mejor tratamiento que influye positivamente en el crecimiento y desarrollo de plantulas en el cultivo de Maíz morado.

fb <- plantula %>% 
  select(!contains("fres"))

rsl <- 5:length(fb) %>% map(\(x) {
  
trait <- names(fb)[x]

cat("\n### ", trait)

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

cat("\n#### ",  "Diagnostico")

rmout$diagplot %>% print()

cat("\n#### ", "Outliers")

rmout$outliers  %>% kable() %>% print()

cat("\n#### ", "ANOVA")

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model) %>% anova_table %>% kable() %>% print()

cat("\n#### ", "Mean comparison")

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group") %>% 
  rename({{trait}} := "emmean")

mc %>% kable() %>% print()

plot <- mc %>% 
  plot_smr(x = "trat"
           , y = trait
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Treatments"
           , glab = "Variety"
           )

plot

list(mc = mc, plot = plot)
  
})

6.3.1 raiz_lgtd

6.3.1.1 Diagnostico

6.3.1.2 Outliers

index	bloque	trat	variedad	raiz_lgtd	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

6.3.1.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
bloque	2	7.20952380952369	3.60476190476185	0.419160753734702	0.658192586259791	ns
trat	6	113.028571428572	18.8380952380953	2.19048869455021	0.0455341138068884	*
variedad	1	2.30476190476197	2.30476190476197	0.267997100142146	0.605268321276777	ns
trat:variedad	6	240.761904761906	40.1269841269843	4.66595502175847	0.000186149204512116	***
Residuals	194	1668.39047619048	8.59995090819833
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.1.4 Mean comparison

	variedad	trat	raiz_lgtd	SE	df	lower.CL	upper.CL	group
1	Hybrid	T0	16.00000	0.7571856	194	14.506627	17.49337	a
2	Creole	T0	11.00000	0.7571856	194	9.506627	12.49337	b
4	Creole	T1	14.00000	0.7571856	194	12.506627	15.49337	a
3	Hybrid	T1	13.93333	0.7571856	194	12.439961	15.42671	a
5	Hybrid	T2	13.60000	0.7571856	194	12.106627	15.09337	a
6	Creole	T2	13.33333	0.7571856	194	11.839961	14.82671	a
8	Creole	T3	12.93333	0.7571856	194	11.439961	14.42671	a
7	Hybrid	T3	12.46667	0.7571856	194	10.973294	13.96004	a
10	Creole	T4	15.13333	0.7571856	194	13.639961	16.62671	a
9	Hybrid	T4	14.00000	0.7571856	194	12.506627	15.49337	a
12	Creole	T5	15.80000	0.7571856	194	14.306627	17.29337	a
11	Hybrid	T5	13.40000	0.7571856	194	11.906627	14.89337	b
13	Hybrid	T6	15.06667	0.7571856	194	13.573294	16.56004	a
14	Creole	T6	14.80000	0.7571856	194	13.306627	16.29337	a

6.3.2 gsr_raiz

6.3.2.1 Diagnostico

6.3.2.2 Outliers

index	bloque	trat	variedad	gsr_raiz	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

6.3.2.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
bloque	2	0.041745714285714	0.020872857142857	0.527763692800836	0.590767266932123	ns
trat	6	2.85575333333334	0.47595888888889	12.0344722862891	0.0000000000176542819668879	***
variedad	1	0.327257619047619	0.327257619047619	8.27460698569895	0.00447016897110515	**
trat:variedad	6	0.701705714285714	0.116950952380952	2.95706841103296	0.00874560396951841	**
Residuals	194	7.67262761904762	0.0395496269023073
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.2.4 Mean comparison

	variedad	trat	gsr_raiz	SE	df	lower.CL	upper.CL	group
2	Creole	T0	1.2560000	0.0513482	194	1.1547275	1.3572725	a
1	Hybrid	T0	1.0853333	0.0513482	194	0.9840609	1.1866058	b
3	Hybrid	T1	0.8926667	0.0513482	194	0.7913942	0.9939391	a
4	Creole	T1	0.7486667	0.0513482	194	0.6473942	0.8499391	b
5	Hybrid	T2	0.9500000	0.0513482	194	0.8487275	1.0512725	a
6	Creole	T2	0.9260000	0.0513482	194	0.8247275	1.0272725	a
8	Creole	T3	0.8600000	0.0513482	194	0.7587275	0.9612725	a
7	Hybrid	T3	0.7760000	0.0513482	194	0.6747275	0.8772725	a
10	Creole	T4	0.9846667	0.0513482	194	0.8833942	1.0859391	a
9	Hybrid	T4	0.7840000	0.0513482	194	0.6827275	0.8852725	b
12	Creole	T5	1.1066667	0.0513482	194	1.0053942	1.2079391	a
11	Hybrid	T5	0.9280000	0.0513482	194	0.8267275	1.0292725	b
14	Creole	T6	1.0513333	0.0513482	194	0.9500609	1.1526058	a
13	Hybrid	T6	0.9646667	0.0513482	194	0.8633942	1.0659391	a

6.3.3 num_raiz

6.3.3.1 Diagnostico

6.3.3.2 Outliers

index	bloque	trat	variedad	num_raiz	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

6.3.3.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
bloque	2	16.8285714285716	8.4142857142858	1.07887001240016	0.342010330536742	ns
trat	6	457.161904761905	76.1936507936509	9.7694620515435	0.0000000021095513439047	***
variedad	1	4.28571428571414	4.28571428571414	0.549509344176629	0.459414622986879	ns
trat:variedad	6	153.714285714286	25.6190476190477	3.28484474630041	0.00422788227377547	**
Residuals	194	1513.0380952381	7.79916543937164
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.3.4 Mean comparison

	variedad	trat	num_raiz	SE	df	lower.CL	upper.CL	group
1	Hybrid	T0	11.33333	0.7210717	194	9.911187	12.75548	a
2	Creole	T0	10.86667	0.7210717	194	9.444520	12.28881	a
3	Hybrid	T1	15.73333	0.7210717	194	14.311187	17.15548	a
4	Creole	T1	14.53333	0.7210717	194	13.111187	15.95548	a
6	Creole	T2	15.06667	0.7210717	194	13.644520	16.48881	a
5	Hybrid	T2	12.00000	0.7210717	194	10.577854	13.42215	b
7	Hybrid	T3	12.53333	0.7210717	194	11.111187	13.95548	a
8	Creole	T3	10.66667	0.7210717	194	9.244520	12.08881	a
10	Creole	T4	13.40000	0.7210717	194	11.977854	14.82215	a
9	Hybrid	T4	10.93333	0.7210717	194	9.511187	12.35548	b
11	Hybrid	T5	11.80000	0.7210717	194	10.377854	13.22215	a
12	Creole	T5	11.33333	0.7210717	194	9.911187	12.75548	a
14	Creole	T6	10.73333	0.7210717	194	9.311187	12.15548	a
13	Hybrid	T6	10.26667	0.7210717	194	8.844520	11.68881	a

6.3.4 peso_seco_raiz

6.3.4.1 Diagnostico

6.3.4.2 Outliers

	index	bloque	trat	variedad	peso_seco_raiz	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag
124	124	1	T1	Hybrid	2.60	1.300138	4.664528	0.0000031	0.00000309326204	0.0006465	OUTLIER
153	153	1	T3	Hybrid	2.88	1.497472	5.372504	0.0000001	0.00000007765065	0.0000163	OUTLIER
193	193	3	T5	Hybrid	1.94	1.218563	4.371860	0.0000123	0.00001231926601	0.0025624	OUTLIER

6.3.4.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
bloque	2	0.708888244834166	0.354444122417083	3.68543486170346	0.0268873108796916	*
trat	6	8.94085063640856	1.49014177273476	15.4941783225705	0.0000000000000187491544735756	***
variedad	1	0.203977311319524	0.203977311319524	2.12091285082425	0.146941784139003	ns
trat:variedad	6	0.268201941388118	0.0447003235646863	0.46478449034871	0.833811880880163	ns
Residuals	191	18.3692915278854	0.0961743011931172
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.4.4 Mean comparison

	variedad	trat	peso_seco_raiz	SE	df	lower.CL	upper.CL	group
1	Hybrid	T0	0.6646667	0.0800726	191	0.5067265	0.8226068	a
2	Creole	T0	0.6346667	0.0800726	191	0.4767265	0.7926068	a
4	Creole	T1	1.2140000	0.0800726	191	1.0560599	1.3719401	a
3	Hybrid	T1	1.1876781	0.0829119	191	1.0241376	1.3512186	a
5	Hybrid	T2	0.8340000	0.0800726	191	0.6760599	0.9919401	a
6	Creole	T2	0.8133333	0.0800726	191	0.6553932	0.9712735	a
7	Hybrid	T3	1.2562495	0.0829119	191	1.0927090	1.4197900	a
8	Creole	T3	1.0566667	0.0800726	191	0.8987265	1.2146068	a
9	Hybrid	T4	0.9240000	0.0800726	191	0.7660599	1.0819401	a
10	Creole	T4	0.8433333	0.0800726	191	0.6853932	1.0012735	a
11	Hybrid	T5	0.6809488	0.0829116	191	0.5174088	0.8444889	a
12	Creole	T5	0.5526667	0.0800726	191	0.3947265	0.7106068	a
13	Hybrid	T6	0.9426667	0.0800726	191	0.7847265	1.1006068	a
14	Creole	T6	0.9320000	0.0800726	191	0.7740599	1.0899401	a

6.3.5 alt_planta

6.3.5.1 Diagnostico

6.3.5.2 Outliers

index	bloque	trat	variedad	alt_planta	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

6.3.5.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
bloque	2	135.895238095238	67.947619047619	2.57285007649253	0.0789200270213045	ns
trat	6	8203.78095238094	1367.29682539682	51.7729655743322	0.0000000000000000000000000000000000000994591853425956	***
variedad	1	20.742857142857	20.742857142857	0.785432401233541	0.376582109302841	ns
trat:variedad	6	1204.52380952381	200.753968253968	7.60158883884396	0.000000244031006032972	***
Residuals	194	5123.43809523809	26.4094747177221
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.5.4 Mean comparison

	variedad	trat	alt_planta	SE	df	lower.CL	upper.CL	group
2	Creole	T0	30.80000	1.326888	194	28.18302	33.41698	a
1	Hybrid	T0	26.13333	1.326888	194	23.51636	28.75031	b
3	Hybrid	T1	46.33333	1.326888	194	43.71636	48.95031	a
4	Creole	T1	40.26667	1.326888	194	37.64969	42.88364	b
5	Hybrid	T2	44.20000	1.326888	194	41.58302	46.81698	a
6	Creole	T2	39.40000	1.326888	194	36.78302	42.01698	b
8	Creole	T3	38.26667	1.326888	194	35.64969	40.88364	a
7	Hybrid	T3	37.06667	1.326888	194	34.44969	39.68364	a
10	Creole	T4	40.53333	1.326888	194	37.91636	43.15031	a
9	Hybrid	T4	33.06667	1.326888	194	30.44969	35.68364	b
11	Hybrid	T5	30.26667	1.326888	194	27.64969	32.88364	a
12	Creole	T5	27.53333	1.326888	194	24.91636	30.15031	a
13	Hybrid	T6	28.80000	1.326888	194	26.18302	31.41698	a
14	Creole	T6	24.66667	1.326888	194	22.04969	27.28364	b

6.3.6 gsr_tallo

6.3.6.1 Diagnostico

6.3.6.2 Outliers

	index	bloque	trat	variedad	gsr_tallo	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag
139	139	1	T2	Hybrid	3.04	-2.328344	-4.180425	0.0000291	0.0000290965	0.0061103	OUTLIER

6.3.6.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
bloque	2	5.39985048550228	2.69992524275114	7.27885665755039	0.000896145755015216	***
trat	6	36.0290245706251	6.00483742843752	16.1887263400496	0.00000000000000461731449963955	***
variedad	1	1.79298499200426	1.79298499200426	4.8337933729749	0.0290959732837517	*
trat:variedad	6	6.51630589531172	1.08605098255195	2.92793641083642	0.00933526126495123	**
Residuals	193	71.5889316642121	0.370927107068456
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.6.4 Mean comparison

	variedad	trat	gsr_tallo	SE	df	lower.CL	upper.CL	group
2	Creole	T0	4.471333	0.1572529	193	4.161179	4.781488	a
1	Hybrid	T0	3.890667	0.1572529	193	3.580512	4.200821	b
3	Hybrid	T1	4.917333	0.1572529	193	4.607178	5.227488	a
4	Creole	T1	4.849333	0.1572529	193	4.539179	5.159488	a
5	Hybrid	T2	5.378330	0.1628281	193	5.057179	5.699481	a
6	Creole	T2	5.032000	0.1572529	193	4.721845	5.342155	a
8	Creole	T3	4.509333	0.1572529	193	4.199179	4.819488	a
7	Hybrid	T3	4.016000	0.1572529	193	3.705845	4.326155	b
10	Creole	T4	4.316667	0.1572529	193	4.006512	4.626822	a
9	Hybrid	T4	3.704000	0.1572529	193	3.393845	4.014155	b
12	Creole	T5	4.195333	0.1572529	193	3.885178	4.505488	a
11	Hybrid	T5	4.066667	0.1572529	193	3.756512	4.376822	a
13	Hybrid	T6	4.218000	0.1572529	193	3.907845	4.528155	a
14	Creole	T6	4.095333	0.1572529	193	3.785178	4.405488	a

6.3.7 nhp_hoja

6.3.7.1 Diagnostico

6.3.7.2 Outliers

	index	bloque	trat	variedad	nhp_hoja	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag
4	4	1	T0	Creole	6	0.9558691	4.835436	0.0000013	0.0000013285389	0.0002763	OUTLIER
5	5	1	T0	Creole	4	-1.0441309	-5.281925	0.0000001	0.0000001278335	0.0000268	OUTLIER
51	51	2	T3	Creole	6	0.8721830	4.412096	0.0000102	0.0000102374945	0.0020987	OUTLIER
57	57	3	T3	Creole	6	0.9052812	4.579529	0.0000047	0.0000046602472	0.0009647	OUTLIER
66	66	2	T4	Creole	6	0.7388497	3.737605	0.0001858	0.0001857817174	0.0369706	OUTLIER
72	72	3	T4	Creole	6	0.7719479	3.905038	0.0000942	0.0000942105812	0.0188421	OUTLIER
88	88	3	T5	Creole	6	0.9719479	4.916774	0.0000009	0.0000008798196	0.0001839	OUTLIER
152	152	1	T3	Hybrid	6	0.8225357	4.160946	0.0000317	0.0000316932621	0.0064337	OUTLIER
155	155	1	T3	Hybrid	6	0.8225357	4.160946	0.0000317	0.0000316932621	0.0064337	OUTLIER
166	166	1	T4	Hybrid	6	0.8225357	4.160946	0.0000317	0.0000316932621	0.0064337	OUTLIER
173	173	2	T4	Hybrid	6	0.8721830	4.412096	0.0000102	0.0000102374945	0.0020987	OUTLIER
181	181	1	T5	Hybrid	6	0.8892024	4.498191	0.0000069	0.0000068534130	0.0014118	OUTLIER

6.3.7.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
bloque	2	0.684993592642831	0.342496796321415	3.22074419272831	0.0422116625843577	*
trat	6	13.0076855867699	2.16794759779498	20.3867735719924	0.00000000000000000344316718685895	***
variedad	1	0.271082180181899	0.271082180181899	2.54918109293389	0.112086289536158	ns
trat:variedad	6	0.722601130029324	0.120433521671554	1.13252319350008	0.345154455799285	ns
Residuals	182	19.3540415507802	0.106340887641649
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.7.4 Mean comparison

	variedad	trat	nhp_hoja	SE	df	lower.CL	upper.CL	group
2	Creole	T0	5.010904	0.0905876	182	4.832167	5.189641	a
1	Hybrid	T0	4.666667	0.0841985	182	4.500536	4.832797	b
4	Creole	T1	5.533333	0.0841985	182	5.367203	5.699464	a
3	Hybrid	T1	5.466667	0.0841985	182	5.300536	5.632797	a
5	Hybrid	T2	5.533333	0.0841985	182	5.367203	5.699464	a
6	Creole	T2	5.533333	0.0841985	182	5.367203	5.699464	a
7	Hybrid	T3	5.010904	0.0905876	182	4.832167	5.189641	a
8	Creole	T3	4.994548	0.0904797	182	4.816024	5.173072	a
10	Creole	T4	5.148394	0.0904797	182	4.969870	5.326918	a
9	Hybrid	T4	5.005028	0.0904787	182	4.826506	5.183550	a
11	Hybrid	T5	5.005063	0.0871860	182	4.833037	5.177088	a
12	Creole	T5	4.995331	0.0871851	182	4.823308	5.167355	a
13	Hybrid	T6	5.000000	0.0841985	182	4.833869	5.166131	a
14	Creole	T6	5.000000	0.0841985	182	4.833869	5.166131	a

6.3.8 larg_hoja

6.3.8.1 Diagnostico

6.3.8.2 Outliers

index	bloque	trat	variedad	larg_hoja	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

6.3.8.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
bloque	2	24.2666666666667	12.1333333333333	0.91282316442606	0.403106357049745	ns
trat	6	3517.25714285715	586.209523809525	44.1021125720196	0.000000000000000000000000000000000935746697641098	***
variedad	1	12.8761904761905	12.8761904761905	0.968710296941942	0.326227923148263	ns
trat:variedad	6	830.857142857142	138.47619047619	10.4179347023194	0.000000000526043977707136	***
Residuals	194	2578.66666666667	13.2920962199313
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.8.4 Mean comparison

	variedad	trat	larg_hoja	SE	df	lower.CL	upper.CL	group
2	Creole	T0	26.20000	0.94135	194	24.34341	28.05659	a
1	Hybrid	T0	22.66667	0.94135	194	20.81007	24.52326	b
3	Hybrid	T1	36.26667	0.94135	194	34.41007	38.12326	a
4	Creole	T1	28.33333	0.94135	194	26.47674	30.18993	b
6	Creole	T2	34.80000	0.94135	194	32.94341	36.65659	a
5	Hybrid	T2	33.86667	0.94135	194	32.01007	35.72326	a
7	Hybrid	T3	28.93333	0.94135	194	27.07674	30.78993	a
8	Creole	T3	28.60000	0.94135	194	26.74341	30.45659	a
10	Creole	T4	28.26667	0.94135	194	26.41007	30.12326	a
9	Hybrid	T4	23.33333	0.94135	194	21.47674	25.18993	b
11	Hybrid	T5	26.46667	0.94135	194	24.61007	28.32326	a
12	Creole	T5	23.40000	0.94135	194	21.54341	25.25659	b
13	Hybrid	T6	23.06667	0.94135	194	21.21007	24.92326	a
14	Creole	T6	21.53333	0.94135	194	19.67674	23.38993	a

6.3.9 grs_hoja

6.3.9.1 Diagnostico

6.3.9.2 Outliers

	index	bloque	trat	variedad	grs_hoja	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag
193	193	3	T5	Hybrid	1.43	0.5793333	3.757259	0.0001718	0.0001717844	0.0359029	OUTLIER
197	197	1	T6	Hybrid	1.45	0.6780000	4.397161	0.0000110	0.0000109676	0.0023032	OUTLIER

6.3.9.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
bloque	2	0.0676350949593889	0.0338175474796944	1.18553751980215	0.307808659010028	ns
trat	6	1.87309077321147	0.312181795535245	10.9441180448838	0.00000000017837157586309	***
variedad	1	0.000660363330975994	0.000660363330975994	0.0231502744556993	0.879226703591861	ns
trat:variedad	6	0.634360294681688	0.105726715780281	3.70644821237387	0.00164817394159611	**
Residuals	192	5.47681453150879	0.0285250756849416
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.9.4 Mean comparison

	variedad	trat	grs_hoja	SE	df	lower.CL	upper.CL	group
2	Creole	T0	0.9700000	0.0436082	192	0.8839874	1.0560126	a
1	Hybrid	T0	0.8846667	0.0436082	192	0.7986541	0.9706793	a
3	Hybrid	T1	0.8446667	0.0436082	192	0.7586541	0.9306793	a
4	Creole	T1	0.6346667	0.0436082	192	0.5486541	0.7206793	b
5	Hybrid	T2	0.7553333	0.0436082	192	0.6693207	0.8413459	a
6	Creole	T2	0.7006667	0.0436082	192	0.6146541	0.7866793	a
7	Hybrid	T3	0.6880000	0.0436082	192	0.6019874	0.7740126	a
8	Creole	T3	0.6300000	0.0436082	192	0.5439874	0.7160126	a
10	Creole	T4	0.6853333	0.0436082	192	0.5993207	0.7713459	a
9	Hybrid	T4	0.6366667	0.0436082	192	0.5506541	0.7226793	a
12	Creole	T5	0.9406667	0.0436082	192	0.8546541	1.0266793	a
11	Hybrid	T5	0.8075459	0.0451543	192	0.7184838	0.8966081	b
14	Creole	T6	0.8126667	0.0436082	192	0.7266541	0.8986793	a
13	Hybrid	T6	0.7247584	0.0451543	192	0.6356962	0.8138205	a

6.3.10 anch_hoja

6.3.10.1 Diagnostico

6.3.10.2 Outliers

index	bloque	trat	variedad	anch_hoja	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

6.3.10.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
bloque	2	15.7079895238095	7.85399476190473	1.13598726704289	0.323231382586704	ns
trat	6	291.914404761905	48.6524007936508	7.0369932102235	0.000000862224352741745	***
variedad	1	0.350554285714286	0.350554285714286	0.0507035231179815	0.822080596652288	ns
trat:variedad	6	139.877105714286	23.3128509523809	3.3719276168642	0.00348053301722631	**
Residuals	194	1341.27822380952	6.91380527736868
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.10.4 Mean comparison

	variedad	trat	anch_hoja	SE	df	lower.CL	upper.CL	group
2	Creole	T0	18.86800	0.6789112	194	17.52901	20.20699	a
1	Hybrid	T0	16.61133	0.6789112	194	15.27234	17.95033	b
3	Hybrid	T1	20.04333	0.6789112	194	18.70434	21.38233	a
4	Creole	T1	18.95867	0.6789112	194	17.61967	20.29766	a
5	Hybrid	T2	21.76933	0.6789112	194	20.43034	23.10833	a
6	Creole	T2	19.03467	0.6789112	194	17.69567	20.37366	b
8	Creole	T3	19.05067	0.6789112	194	17.71167	20.38966	a
7	Hybrid	T3	17.51800	0.6789112	194	16.17901	18.85699	a
10	Creole	T4	17.06933	0.6789112	194	15.73034	18.40833	a
9	Hybrid	T4	16.07400	0.6789112	194	14.73501	17.41299	a
11	Hybrid	T5	19.51800	0.6789112	194	18.17901	20.85699	a
12	Creole	T5	18.28667	0.6789112	194	16.94767	19.62566	a
13	Hybrid	T6	17.86067	0.6789112	194	16.52167	19.19966	a
14	Creole	T6	17.55467	0.6789112	194	16.21567	18.89366	a

6.3.11 peso_seco_brote

6.3.11.1 Diagnostico

6.3.11.2 Outliers

	index	bloque	trat	variedad	peso_seco_brote	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag
42	42	3	T2	Creole	2.39	1.216944	3.686305	0.0002275	0.00022753340922	0.0464168	OUTLIER
72	72	3	T4	Creole	3.09	1.776277	5.380610	0.0000001	0.00000007423391	0.0000156	OUTLIER
127	127	2	T1	Hybrid	3.27	1.233120	3.735306	0.0001875	0.00018748728839	0.0384349	OUTLIER
134	134	3	T1	Hybrid	0.73	-1.285056	-3.892628	0.0000992	0.00009916401076	0.0204278	OUTLIER
169	169	1	T4	Hybrid	2.85	1.391936	4.216384	0.0000248	0.00002482511247	0.0051388	OUTLIER
170	170	1	T4	Hybrid	2.93	1.471936	4.458716	0.0000082	0.00000824521585	0.0017150	OUTLIER
172	172	2	T4	Hybrid	2.98	1.503120	4.553176	0.0000053	0.00000528419693	0.0011044	OUTLIER

6.3.11.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
bloque	2	0.502411537322084	0.251205768661042	1.46529227890307	0.233650440367885	ns
trat	6	43.010518479078	7.168419746513	41.8136500705716	0.0000000000000000000000000000000424415542003033	***
variedad	1	0.0341827573920979	0.0341827573920979	0.199389252664187	0.655730939528593	ns
trat:variedad	6	3.43432182335683	0.572386970559472	3.33875377534505	0.00378990456664722	**
Residuals	187	32.0587772255111	0.17143731136637
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.11.4 Mean comparison

	variedad	trat	peso_seco_brote	SE	df	lower.CL	upper.CL	group
2	Creole	T0	0.7100000	0.1069072	187	0.4991008	0.9208992	a
1	Hybrid	T0	0.4253333	0.1069072	187	0.2144341	0.6362325	a
3	Hybrid	T1	2.0300403	0.1148803	187	1.8034124	2.2566683	a
4	Creole	T1	1.5046667	0.1069072	187	1.2937675	1.7155659	b
5	Hybrid	T2	1.2113333	0.1069072	187	1.0004341	1.4222325	a
6	Creole	T2	1.0926154	0.1106987	187	0.8742365	1.3109942	a
8	Creole	T3	1.7186667	0.1069072	187	1.5077675	1.9295659	a
7	Hybrid	T3	1.4093333	0.1069072	187	1.1984341	1.6202325	b
10	Creole	T4	1.1933296	0.1106987	187	0.9749508	1.4117085	a
9	Hybrid	T4	1.0985717	0.1196740	187	0.8624872	1.3346563	a
12	Creole	T5	0.6500000	0.1069072	187	0.4391008	0.8608992	a
11	Hybrid	T5	0.6373333	0.1069072	187	0.4264341	0.8482325	a
14	Creole	T6	0.5486667	0.1069072	187	0.3377675	0.7595659	a
13	Hybrid	T6	0.4586667	0.1069072	187	0.2477675	0.6695659	a

6.3.12 Figure 2

legend <- cowplot::get_plot_component(rsl[[1]]$plot, 'guide-box-top', return_all = TRUE)

fig <- list(
  rsl[[1]]$plot + labs(x = NULL, y = "Root length (cm)") + 
    scale_y_continuous(expand = c(0, 0), limits = c(0, 20), n.breaks = 5) +
    theme(legend.position="none"
          , axis.title.x=element_blank()
          , axis.text.x=element_blank()
          , axis.ticks.x=element_blank())
  
  , rsl[[2]]$plot + labs(x = NULL, y = "Root thickness (mm)") + 
    scale_y_continuous(expand = c(0, 0), limits = c(0, 2), n.breaks = 5) +
    theme(legend.position="none"
          , axis.title.x=element_blank()
          , axis.text.x=element_blank()
          , axis.ticks.x=element_blank())
  
  , rsl[[3]]$plot  + labs(x = NULL, y = "Root number") + 
    scale_y_continuous(expand = c(0, 0), limits = c(0, 20), n.breaks = 5) +
    theme(legend.position="none"
          , axis.title.x=element_blank()
          , axis.text.x=element_blank()
          , axis.ticks.x=element_blank())
  
  , rsl[[4]]$plot + labs(y = "Root dry weight (g)") + 
    scale_y_continuous(expand = c(0, 0), limits = c(0, 2), n.breaks = 5) +
    theme(legend.position="none") 
  ) %>% 
  plot_grid(plotlist = .
            , ncol = 1
            , labels = "auto"
              ) 

plot <- list(legend, fig) %>% 
   plot_grid(plotlist = .
            , ncol = 1
            , rel_heights = c(0.05, 1)
              ) 

plot %>% 
  ggsave2(plot = .
          , "files/Fig-2.jpg"
          , units = "cm"
          , width = 12
          , height = 24
         )

plot %>% 
  ggsave2(plot = .
          , "files/Fig-2.eps"
          , units = "cm"
          , width = 12
          , height = 24
         )

include_graphics("files/Fig-2.jpg")

6.3.13 Table

tab <- 5:length(rsl) %>% map(\(x) { 
  
  trait <- names(rsl[[x]]$mc)[[3]]
  
  rsl[[x]]$mc %>% 
    mutate(across(where(is.numeric), ~ round(., 2))) %>% 
    unite({{trait}}, c({{trait}}, group), sep = " ") %>% 
    select(1:3)
  
  }) %>% 
  Reduce(function(...) merge(..., all = TRUE), .) %>% 
  rename(Variety = "variedad" 
         , Treatment = trat
         , "Plant height (cm)" = "alt_planta"
         , "Stem thickness (mm)" = "gsr_tallo"
         , "Leaves number" = "nhp_hoja"
         , "Leaf length (cm)" = "larg_hoja" 
         , "Leaf thickness (mm)" = "grs_hoja"
         , "Leaf width (mm)" = "anch_hoja"
         , "Shoot Dry weight (g)" = "peso_seco_brote"
         ) %>% 
  arrange(Treatment)

tab %>% kable()

Variety	Treatment	Plant height (cm)	Stem thickness (mm)	Leaves number	Leaf length (cm)	Leaf thickness (mm)	Leaf width (mm)	Shoot Dry weight (g)
Creole	T0	30.8 a	4.47 a	5.01 a	26.2 a	0.97 a	18.87 a	0.71 a
Hybrid	T0	26.13 b	3.89 b	4.67 b	22.67 b	0.88 a	16.61 b	0.43 a
Creole	T1	40.27 b	4.85 a	5.53 a	28.33 b	0.63 b	18.96 a	1.5 b
Hybrid	T1	46.33 a	4.92 a	5.47 a	36.27 a	0.84 a	20.04 a	2.03 a
Creole	T2	39.4 b	5.03 a	5.53 a	34.8 a	0.7 a	19.03 b	1.09 a
Hybrid	T2	44.2 a	5.38 a	5.53 a	33.87 a	0.76 a	21.77 a	1.21 a
Creole	T3	38.27 a	4.51 a	4.99 a	28.6 a	0.63 a	19.05 a	1.72 a
Hybrid	T3	37.07 a	4.02 b	5.01 a	28.93 a	0.69 a	17.52 a	1.41 b
Creole	T4	40.53 a	4.32 a	5.15 a	28.27 a	0.69 a	17.07 a	1.19 a
Hybrid	T4	33.07 b	3.7 b	5.01 a	23.33 b	0.64 a	16.07 a	1.1 a
Creole	T5	27.53 a	4.2 a	5 a	23.4 b	0.94 a	18.29 a	0.65 a
Hybrid	T5	30.27 a	4.07 a	5.01 a	26.47 a	0.81 b	19.52 a	0.64 a
Creole	T6	24.67 b	4.1 a	5 a	21.53 a	0.81 a	17.55 a	0.55 a
Hybrid	T6	28.8 a	4.22 a	5 a	23.07 a	0.72 a	17.86 a	0.46 a

tab %>% sheet_write(data = ., gs, "table")

6.3.14 PCA

blues <- 1:length(rsl) %>% map(\(x) { 
  
  rsl[[x]]$mc %>% 
    select(1:3)
  
  }) %>% 
  Reduce(function(...) merge(..., all = TRUE), .) %>% 
    rename(Variety = "variedad" 
         , Treatment = trat
         , "Plant height (cm)" = "alt_planta"
         , "Stem thickness (mm)" = "gsr_tallo"
         , "Leaves number" = "nhp_hoja"
         , "Leaf length (cm)" = "larg_hoja" 
         , "Leaf thickness (mm)" = "grs_hoja"
         , "Leaf width (mm)" = "anch_hoja"
         , "Shoot Dry weight (g)" = "peso_seco_brote"
         #>
         , "Root length (cm)" = "raiz_lgtd"
         , "Root thickness (mm)" = "gsr_raiz"
         , "Root number" = "num_raiz"
         , "Root Dry weight (g)" = peso_seco_raiz
         )
  
blues %>% str()
## 'data.frame':    14 obs. of  13 variables:
##  $ Variety             : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 2 2 2 ...
##  $ Treatment           : Factor w/ 7 levels "T0","T1","T2",..: 1 2 3 4 5 6 7 1 2 3 ...
##  $ Root length (cm)    : num  11 14 13.3 12.9 15.1 ...
##  $ Root thickness (mm) : num  1.256 0.749 0.926 0.86 0.985 ...
##  $ Root number         : num  10.9 14.5 15.1 10.7 13.4 ...
##  $ Root Dry weight (g) : num  0.635 1.214 0.813 1.057 0.843 ...
##  $ Plant height (cm)   : num  30.8 40.3 39.4 38.3 40.5 ...
##  $ Stem thickness (mm) : num  4.47 4.85 5.03 4.51 4.32 ...
##  $ Leaves number       : num  5.01 5.53 5.53 4.99 5.15 ...
##  $ Leaf length (cm)    : num  26.2 28.3 34.8 28.6 28.3 ...
##  $ Leaf thickness (mm) : num  0.97 0.635 0.701 0.63 0.685 ...
##  $ Leaf width (mm)     : num  18.9 19 19 19.1 17.1 ...
##  $ Shoot Dry weight (g): num  0.71 1.5 1.09 1.72 1.19 ...

pca <- blues %>% 
  select(!c("Leaves number")) %>% 
  unite("treat", c(Treatment, Variety), remove = F, sep = "-") %>% 
  column_to_rownames("treat") %>% 
  PCA(scale.unit = T, quali.sup = c(1:2), graph = F)

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, quali.sup = c(1:2), graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3  Dim.4  Dim.5  Dim.6  Dim.7  Dim.8
## Variance               5.26   2.29   0.98   0.58   0.34   0.24   0.19   0.06
## % of var.             52.62  22.93   9.78   5.75   3.44   2.40   1.90   0.63
## Cumulative % of var.  52.62  75.55  85.33  91.09  94.53  96.93  98.83  99.46
##                       Dim.9 Dim.10
## Variance               0.03   0.02
## % of var.              0.33   0.21
## Cumulative % of var.  99.79 100.00
## 
## Individuals
##                         Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3
## T0-Creole            |  4.11 | -1.80  4.39  0.19 |  2.83 25.00  0.48 | -2.05
## T1-Creole            |  3.21 |  2.73 10.15  0.73 | -1.24  4.77  0.15 |  0.45
## T2-Creole            |  2.92 |  2.18  6.45  0.56 |  1.03  3.29  0.12 |  0.62
## T3-Creole            |  2.55 |  1.55  3.26  0.37 | -0.93  2.68  0.13 | -1.33
## T4-Creole            |  1.93 |  0.33  0.15  0.03 | -0.68  1.44  0.12 |  1.22
## T5-Creole            |  3.41 | -2.87 11.15  0.71 |  1.25  4.88  0.13 |  1.14
## T6-Creole            |  2.81 | -2.57  9.00  0.84 | -0.45  0.64  0.03 |  0.00
## T0-Hybrid            |  3.66 | -3.42 15.88  0.87 | -0.03  0.00  0.00 |  1.23
## T1-Hybrid            |  4.47 |  3.94 21.11  0.78 |  0.97  2.95  0.05 |  0.98
## T2-Hybrid            |  3.74 |  2.46  8.24  0.44 |  2.23 15.45  0.36 | -0.13
## T3-Hybrid            |  2.87 |  1.38  2.60  0.23 | -1.96 11.91  0.47 | -1.16
## T4-Hybrid            |  3.07 | -0.95  1.22  0.10 | -2.75 23.47  0.80 | -0.50
## T5-Hybrid            |  1.94 | -1.10  1.65  0.32 |  0.60  1.11  0.09 | -0.53
## T6-Hybrid            |  2.42 | -1.87  4.76  0.60 | -0.88  2.41  0.13 |  0.06
##                        ctr  cos2  
## T0-Creole            30.57  0.25 |
## T1-Creole             1.48  0.02 |
## T2-Creole             2.77  0.04 |
## T3-Creole            12.86  0.27 |
## T4-Creole            10.79  0.40 |
## T5-Creole             9.48  0.11 |
## T6-Creole             0.00  0.00 |
## T0-Hybrid            11.12  0.11 |
## T1-Hybrid             7.05  0.05 |
## T2-Hybrid             0.13  0.00 |
## T3-Hybrid             9.82  0.16 |
## T4-Hybrid             1.83  0.03 |
## T5-Hybrid             2.08  0.08 |
## T6-Hybrid             0.03  0.00 |
## 
## Variables
##                        Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr
## Root length (cm)     | -0.39  2.82  0.15 | -0.23  2.34  0.05 |  0.84 72.56
## Root thickness (mm)  | -0.63  7.63  0.40 |  0.70 21.21  0.49 |  0.01  0.01
## Root number          |  0.75 10.80  0.57 |  0.10  0.47  0.01 |  0.46 21.30
## Root Dry weight (g)  |  0.68  8.80  0.46 | -0.57 13.96  0.32 | -0.12  1.39
## Plant height (cm)    |  0.96 17.35  0.91 |  0.07  0.23  0.01 |  0.08  0.62
## Stem thickness (mm)  |  0.74 10.38  0.55 |  0.55 13.37  0.31 |  0.11  1.13
## Leaf length (cm)     |  0.90 15.41  0.81 |  0.35  5.27  0.12 |  0.08  0.60
## Leaf thickness (mm)  | -0.51  5.00  0.26 |  0.72 22.60  0.52 |  0.08  0.65
## Leaf width (mm)      |  0.58  6.43  0.34 |  0.67 19.31  0.44 | -0.12  1.50
## Shoot Dry weight (g) |  0.90 15.38  0.81 | -0.17  1.22  0.03 | -0.05  0.25
##                       cos2  
## Root length (cm)      0.71 |
## Root thickness (mm)   0.00 |
## Root number           0.21 |
## Root Dry weight (g)   0.01 |
## Plant height (cm)     0.01 |
## Stem thickness (mm)   0.01 |
## Leaf length (cm)      0.01 |
## Leaf thickness (mm)   0.01 |
## Leaf width (mm)       0.01 |
## Shoot Dry weight (g)  0.00 |
## 
## Supplementary categories
##                         Dist   Dim.1  cos2 v.test   Dim.2  cos2 v.test   Dim.3
## Creole               |  0.41 | -0.06  0.02  -0.10 |  0.26  0.41   0.62 |  0.01
## Hybrid               |  0.41 |  0.06  0.02   0.10 | -0.26  0.41  -0.62 | -0.01
## T0                   |  3.08 | -2.61  0.72  -1.67 |  1.40  0.21   1.36 | -0.41
## T1                   |  3.52 |  3.34  0.90   2.14 | -0.13  0.00  -0.13 |  0.72
## T2                   |  3.02 |  2.32  0.59   1.49 |  1.63  0.29   1.58 |  0.24
## T3                   |  2.44 |  1.47  0.36   0.94 | -1.44  0.35  -1.40 | -1.24
## T4                   |  2.02 | -0.31  0.02  -0.20 | -1.71  0.72  -1.66 |  0.36
## T5                   |  2.36 | -1.98  0.71  -1.27 |  0.92  0.15   0.90 |  0.30
## T6                   |  2.53 | -2.22  0.77  -1.43 | -0.67  0.07  -0.65 |  0.03
##                       cos2 v.test  
## Creole                0.00   0.03 |
## Hybrid                0.00  -0.03 |
## T0                    0.02  -0.60 |
## T1                    0.04   1.07 |
## T2                    0.01   0.36 |
## T3                    0.26  -1.85 |
## T4                    0.03   0.53 |
## T5                    0.02   0.45 |
## T6                    0.00   0.05 |
pcainfo <- factoextra::get_pca_var(pca)
pcainfo$cor
##                           Dim.1       Dim.2        Dim.3       Dim.4
## Root length (cm)     -0.3850142 -0.23160568  0.842557174 -0.23147791
## Root thickness (mm)  -0.6334207  0.69746926  0.009092441  0.15767946
## Root number           0.7538553  0.10399598  0.456517815  0.36200737
## Root Dry weight (g)   0.6804806 -0.56588136 -0.116435572  0.10078095
## Plant height (cm)     0.9555792  0.07280624  0.077966273  0.01295185
## Stem thickness (mm)   0.7388485  0.55381697  0.105138493 -0.26706820
## Leaf length (cm)      0.9003496  0.34780450  0.076577268  0.07725203
## Leaf thickness (mm)  -0.5131467  0.72000262  0.079603893  0.34016346
## Leaf width (mm)       0.5818081  0.66556182 -0.121233647 -0.35752445
## Shoot Dry weight (g)  0.8995921 -0.16715747 -0.049475693  0.18589699
##                             Dim.5
## Root length (cm)      0.156691674
## Root thickness (mm)   0.022040900
## Root number          -0.142026846
## Root Dry weight (g)   0.373757353
## Plant height (cm)    -0.081352886
## Stem thickness (mm)   0.005369376
## Leaf length (cm)     -0.125316855
## Leaf thickness (mm)   0.276041379
## Leaf width (mm)       0.184068744
## Shoot Dry weight (g)  0.164719886
pcainfo$contrib
##                          Dim.1      Dim.2        Dim.3       Dim.4        Dim.5
## Root length (cm)      2.817341  2.3388953 72.555264317  9.31529745  7.127583908
## Root thickness (mm)   7.625538 21.2110668  0.008449503  4.32242925  0.141029240
## Root number          10.800951  0.4715693 21.300295456 22.78308086  5.855869417
## Root Dry weight (g)   8.800706 13.9624989  1.385611659  1.76577167 40.553653983
## Plant height (cm)    17.354792  0.2311264  0.621274762  0.02916359  1.921305491
## Stem thickness (mm)  10.375209 13.3734939  1.129779427 12.40000834  0.008369473
## Leaf length (cm)     15.406653  5.2745177  0.599335367  1.03752237  4.558998802
## Leaf thickness (mm)   5.004596 22.6037514  0.647647639 20.11652502 22.120700640
## Leaf width (mm)       6.433473 19.3147533  1.502161088 22.22230592  9.835820578
## Shoot Dry weight (g) 15.380741  1.2183272  0.250180782  6.00789552  7.876668468

6.3.15 Figure 3

var <- pca %>% 
  plot.PCA(choix = "var"
           , cex = 0.7 
           )

ind <- pca %>% 
  plot.PCA(choix = "ind", habillage = 2
           , label = c("ind")
           , invisible = "quali"
           ) +
  labs(colour = "Treatments") +
  theme(legend.position = "bottom"
        , legend.direction = "horizontal") +
  guides(colour = guide_legend(nrow = 1))
  

fig <- list(var, ind) %>% 
    plot_grid(plotlist = .
              , ncol = 2
              , labels = "auto"
              , rel_widths = c(1.5, 2)
              ) 

fig %>% 
  ggsave2(plot = .
          , "files/Fig-3.jpg"
          , units = "cm"
          , width = 30
          , height = 12
         )

fig %>% 
  ggsave2(plot = .
          , "files/Fig-3.eps"
          , units = "cm"
          , width = 30
          , height = 12
         )

include_graphics("files/Fig-3.jpg")

6.3.16 Principal components

var <- get_pca_var(pca)

tmp <- tempfile(fileext = ".png")
ppi <- 300
png(tmp, width=8*ppi, height=12*ppi, res=ppi)
corrplot(var$cor, 
         method="number",
         tl.col="black", 
         tl.srt=45,)
graphics.off()

pt1 <- png::readPNG(tmp) %>%
  grid::rasterGrob(interpolate = TRUE)

pt2 <- fviz_eig(pca, 
                addlabels=TRUE,
                hjust = 0.05,
                barfill="white",
                barcolor ="darkblue",
                linecolor ="red") + 
  ylim(0, 60) + 
  labs(
    title = "PCA - percentage of explained variances",
    y = "Variance (%)") +
  theme_minimal()

pt3 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 1, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 20) + 
  labs(title = "Dim 1 - variables contribution") 

pt4 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 2, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 25) + 
  labs(title = "Dim 2 - variables contribution") 

plot <- ggdraw(xlim = c(0.0, 1.0), ylim = c(0, 1.0))+
  draw_plot(pt1,  width = 0.4, height = 0.99, x = 0.62, y = 0.0) +  
  draw_plot(pt2,  width = 0.6, height = 0.34, x = 0.03, y = 0.66) +
  draw_plot(pt3, width = 0.6, height = 0.34, x = 0.03, y = 0.33) + 
  draw_plot(pt4, width = 0.6, height = 0.34, x = 0.03, y = 0.0) +
        draw_plot_label(
    label = c("a", "b", "c", "d"),
    x = c(0.005, 0.005, 0.005, 0.65),
    y = c(0.999, 0.67, 0.34, 0.999))

ggsave2(plot = plot, "files/FigS1.jpg", height = 25, width = 46, units = "cm")

ggsave2(plot = plot, "files/FigS1.eps", height = 25, width = 46, units = "cm")

knitr::include_graphics("files/FigS1.jpg")